deforestation-detection

AI-Powered Environmental Monitoring System for detecting deforestation, poaching, and environmental hazards

Fourier Domain Adaptation + Evidential Deep Learning for source-free cross-domain deforestation detection in the Brazilian Amazon using Landsat-8 satellite imagery. ConvNeXt encoder (9.5M params) with per-pixel uncertainty estimation. PyTorch implementation.

AI-powered forest monitoring system that detects deforestation, identifies potential forest fires, and supports wildlife and environmental conservation using intelligent image analysis and real-time alerts.

Deep learning pipeline for detecting deforestation using Sentinel satellite imagery and ResNet.

Trains a CNN on EuroSAT satellite imagery for land-cover classification (93.6% test accuracy), then applies it to real GeoTIFF scenes across two time points to automatically flag Forest → non-forest transitions as candidate deforestation events.

Autonomous Geospatial AI for Early Deforestation Warning, LoRA Fine-Tuning & EUDR Compliance (Regulation EU 2023/1115)

End-to-end geospatial AI platform for automated deforestation detection using Sentinel-2 L2A imagery, PyTorch U-Net segmentation, spatial blocking, and an executive Streamlit dashboard.

A computer vision pipeline that analyzes satellite imagery to detect deforestation. It processes live or archived feeds, compares them against reference data, and alerts the user when changes in forest cover are identified. Built to support real-time environmental monitoring and conservation efforts.